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Demand Planning

Differentiated Capabilities

Demand Planning

A consensus forecast built on accuracy, not authority.

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Conventional demand planning treats stakeholder collaboration as the primary mechanism for reaching a consensus forecast, collecting inputs, surfacing disagreements, and negotiating a number. It is now well established that a heavier reliance on statistical weighting of inputs utilizing historical accuracy measures produces more reliable plans. Collaboration remains essential, but in a different role: clarifying assumptions, supplying the model with information it cannot observe on its own, and discussing what lower probability assumptions should be tested.

Key Features:

  • ML-weighted consensus forecast — sales, marketing, operations, and statistical inputs are each scored on historical accuracy; the final plan weights every source by what it has demonstrably gotten right, not by organizational rank or loudest voice 
  • Sell-In / Sell-Through channel visibility — see what’s selling through to end customers at the distributor tier, not just what’s shipping in; plan against real end-market demand rather than inventory accumulating silently in the channel 
  • Attribute-aware forecasting for configured products — forecast at the attribute level for complex MTO, CTO, and ETO environments without a SKU explosion; demand signals for specific configurations flow directly into supply planning via the same ABP architecture 
  • AI/ML statistical forecasting with configurable causal factors — encoding the information the model cannot directly observe, including promotions, pricing actions, market events, and supplier constraints — with automated exception workflows to surface where human judgment is needed 
  • Currency-to-unit forecast translation at any level of aggregation, disaggregated automatically by any measure 
  • Product lifecycle management from NPI ramp through end-of-life — with phase-in/phase-out curves to prevent forecast distortion at lifecycle transitions 
  • Structured scenario planning for low-probability, high-impact assumptions — allowing teams to test alternative futures, compare plan sensitivity, and align on which risks warrant contingency action 
  • Unlimited parallel hierarchies for drill-up/down across product, customer, geography, or any planning dimension 
  • Excel Pivot Table UI, HTML5 browser interface, and live Power BI connectivity 

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